Text and Speech Analysis detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 6th Sem scheme and its subjects, do visit AI&ML 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&ML Professional Elective-VII syllabus scheme. The detailed syllabus of text and speech analysis is as follows.
Course Objectives:
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Unit I
NATURAL LANGUAGE BASICS
Foundations of natural language processing – Language Syntax and Structure- Text Preprocessing and Wrangling – Text tokenization – Stemming – Lemmatization – Removing stopwords – Feature Engineering for Text representation – Bag of Words model- Bag of N-Grams model – TF-IDF model
Suggested Activities
- Flipped classroom on NLP
- Implementation of Text Preprocessing using NLTK
- Implementation of TF-IDF models
Suggested Evaluation Methods
- Quiz on NLP Basics
- Demonstration of Programs
Unit II
TEXT CLASSIFICATION
Vector Semantics and Embeddings -Word Embeddings – Word2Vec model – Glove model -FastText model – Overview of Deep Learning models – RNN – Transformers – Overview of Text summarization and Topic Models
Suggested Activities
- Flipped classroom on Feature extraction of documents
- Implementation of SVM models for text classification
- External learning: Text summarization and Topic models
Suggested Evaluation Methods
- Assignment on above topics
- Quiz on RNN, Transformers
- Implementing NLP with RNN and Transformers
Unit III
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Unit IV
TEXT-TO-SPEECH SYNTHESIS
Overview. Text normalization. Letter-to-sound. Prosody, Evaluation. Signal processing -Concatenative and parametric approaches, WaveNet and other deep learning-based TTS systems
Suggested Activities:
- Flipped classroom on Speech signal processing
- Exploring Text normalization
- Data collection
- Implementation of TTS systems
Suggested Evaluation Methods
- Assignment on the above topics
- Quiz on wavenet, deep learning-based TTS systems
- Finding accuracy with different TTS systems
Unit V
AUTOMATIC SPEECH RECOGNITION
Speech recognition: Acoustic modelling – Feature Extraction – HMM, HMM-DNN systems
Suggested Activities:
- Flipped classroom on Speech recognition.
- Exploring Feature extraction
Suggested Evaluation Methods
- Assignment on the above topics
- Quiz on acoustic modelling
Practical Exercises
- Create Regular expressions in Python for detecting word patterns and tokenizing text
- Getting started with Python and NLTK – Searching Text, Counting Vocabulary, Frequency Distribution, Collocations, Bigrams
- Accessing Text Corpora using NLTK in Python
- Write a function that finds the 50 most frequently occurring words of a text that are not stop words.
- Implement the Word2Vec model
- Use a transformer for implementing classification
- Design a chatbot with a simple dialog system
- Convert text to speech and find accuracy
- Design a speech recognition system and find the error rate
Course Outcomes:
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Text Books:
- Daniel Jurafsky and James H. Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition”, Third Edition, 2022.
Reference Books:
- Dipanjan Sarkar, “Text Analytics with Python: A Practical Real-World approach to Gaining Actionable insights from your data”, APress,2018.
- Tanveer Siddiqui, Tiwary U S, “Natural Language Processing and Information Retrieval”, Oxford University Press, 2008.
- Lawrence Rabiner, Biing-Hwang Juang, B. Yegnanarayana, “Fundamentals of Speech Recognition” 1st Edition, Pearson, 2009.
- Steven Bird, Ewan Klein, and Edward Loper, “Natural language processing with Python”, O’REILLY.
For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 6th Sem, visit AI&ML 6th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.